Prometheus + Grafana监控体系从零搭建
Building a Monitoring Stack with Prometheus and Grafana from Scratch
| iDev Tech | 2026-08-17T10:00:00
完整教程：从零搭建基于Prometheus和Grafana的应用监控体系，包括指标采集、告警配置和仪表板设计。
Complete tutorial: building an application monitoring system with Prometheus and Grafana, including metrics collection, alerting, and dashboard design.
架构设计监控体系由四层组成：指标采集层（Prometheus Exporter/Micrometer）→ 时序数据库（Prometheus Server）→ 可视化层（Grafana）→ 告警层（AlertManager → Slack/邮件/PagerDuty）。Spring Boot应用通过Micrometer自动暴露JVM、HTTP请求和自定义业务指标。关键指标设计RED方法（请求驱动服务）：Rate（请求速率）、Errors（错误率）、Duration（延迟分布）。USE方法（资源类）：Utilization（使用率）、Saturation（饱和度）、Errors（错误数）。业务指标：订单量、支付成功率、活跃用户数等。告警最佳实践分级告警：P1（立即响应）、P2（30分钟内）、P3（下个工作日）。避免告警疲劳：设置合理的阈值和持续时间，使用抑制规则避免级联告警。告警必须可操作——每条告警都应有对应的Runbook。
Architecture DesignThe monitoring stack has four layers: metrics collection (Prometheus Exporter/Micrometer) → time-series database (Prometheus Server) → visualization (Grafana) → alerting (AlertManager → Slack/Email/PagerDuty). Spring Boot applications auto-expose JVM, HTTP request, and custom business metrics via Micrometer.Key Metrics DesignRED method (request-driven services): Rate, Errors, Duration. USE method (resources): Utilization, Saturation, Errors. Business metrics: order volume, payment success rate, active users, etc.Alerting Best PracticesTiered alerts: P1 (immediate response), P2 (within 30 minutes), P3 (next business day). Avoid alert fatigue: set reasonable thresholds and durations, use inhibition rules to prevent cascading alerts. Every alert must be actionable — each should have a corresponding runbook.